Predicting metal-binding site residues in low-resolution structural models

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.

The type and quantity of dietary fat and carbohydrate alter faecal microbiome and short-chain fatty acid excretion in a metabolic syndrome ‘at-risk’ population syndrome.

An obese-type human microbiota with an increased Firmicutes:Bacteroidetes ratio has been described that may link the gut microbiome with obesity and metabolic syndrome (MetS) development. Dietary fat and carbohydrate are modifiable risk factors that may impact on MetS by altering the human microbiome composition. We determined the effect of the amount and type of dietary fat and carbohydrate on faecal bacteria and short chain fatty acid (SCFA) concentrations in people ‘at risk’ of MetS.

Receptor binding profiles of avian influenza virus hemagglutinin subtypes on human cells as a predictor of pandemic potential

The host adaptation of influenza virus is partly dependent on the sialic acid (SA) isoform bound by the viral hemagglutinin (HA). Avian influenza viruses preferentially bind the α-2,3 SA and human influenza viruses the α-2,6 isoform. Each isoform is predominantly associated with different surface epithelial cell types of the human upper airway. Using recombinant HAs and human tracheal airway epithelial cells in vitro and ex vivo, we show that many avian HA subtypes do not adhere to this canonical view of SA specificity. The propensity of avian viruses to adapt to human receptors may thus be more widespread than previously supposed.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Characterization of Grb2-binding proteins in human platelets activated by Fc gamma RIIA cross-linking.

Glutathione-S-transferase (GST)-Grb2 fusion proteins have been used to identify the potential role of Grb2-binding proteins in platelet activation by the platelet low-affinity IgG receptor, Fc gamma RIIA. Two tyrosine phosphoproteins of 38 and 63 kD bind to the SH2 domain of Grb2 following Fc gamma RIIA stimulation of platelets. Both are located in the particulate fraction following platelet activation and are also able to bind to a GST-construct containing the SH2 and SH3 domains of phospholipase C gamma 1. p38 also forms a complex with the tyrosine kinase csk in stimulated cells and is a substrate for the kinase. The SH3 domains of Grb2 form a stable complex with SOS1 and two proteins of 75 kD and 120 kD, which undergo tyrosine phosphorylation in Fc gamma RIIA stimulated cells. The 75-kD protein is recognized by antibodies to SLP-76, which has recently been isolated from T cells and sequenced. Tyrosine phosphorylation of p38 and p63 is also observed in platelets stimulated by the tyrosine kinase-linked receptor agonist collagen and by the G protein-coupled receptor agonist thrombin, although phosphorylation of SLP-76 is only observed in collagen-stimulated platelets. p38 and p63 may provide a docking site for Grb2, thereby linking Grb2 SH3-binding proteins SOS1, SLP-76, and p120 to downstream signalling events.

Elevated levels of corticotrophin-releasing factor binding protein in the blood of patients suffering from arthritis and septicaemia and the presence of novel ligands in synovial fluid.

In view of the reported inflammatory effects of corticotrophin-releasing factor (CRF) and the associated regulatory elements in the gene of its binding protein (BP), we postulate that both BP as well as novel BP-ligands other than CRF may be involved in inflammatory disease. We have investigated BP in the blood of patients with arthritis and septicaemia and have attempted to identify CRF and other BP-ligands in synovial fluid. The BP was found to be significantly elevated in the blood of patients with rheumatoid arthritis and septicaemia. There was less BP-ligand and CRF in synovial fluid from patients with rheumatoid arthritis that from those with osteo- or psoriatic arthritis. There was at least 10-fold more BP-ligand than CRF in the fluid of all three groups of patients. A small amount of immunoreactive human (h)CRF, eluting in the expected position of CRF-41, was detected after high-pressure liquid chromatography of arthritic synovial fluid; however, the bulk of material with BP-ligand binding activity eluted earlier, suggesting that synovial fluid contained novel peptides that interacted with the BP. These results would suggest that the BP and its ligands could play an endocrine immunomodulatory role in inflammatory disease.